POS0677 THE DEVELOPMENT OF AN OPTIMAL MODEL FOR AUTOCALCULATION OF MSASSS USING DEEP LEARNING IN PATIENTS WITH ANKYLOSING SPONDYLITISDS
Authors: H. H. Chen, D. Y. Chen
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> At present, to detect the structural changes of the spine in ankylosing spondylitis (AS), the most commonly used tool is spine X-ray photography and interpretation by a physician, and a score (modified Stoke Ankylosing Spondylitis Spinal Score, mSASSS) is given. However, because there is more than one spine (12 in total) and each spine joint disease includes at least 5 items, it takes at least 60 minutes to interpret the results of mSASSS and produce scores for each patient's spine. Therefore, because the manual calculation of mSASSS is time-consuming and may introduce interpretation errors, the study aimed to use deep learning to develop an optimal model for auto-calculation of mSASSS in patients with AS. <h3>Methods</h3> We collected 1056 pairs of cervical spine and lumbar spine radiographs in 646 patients with AS at Taichung Veterans General Hospital (TCVGH) from Aug 1, 2001, to Dec 30, 2020. We designed software for annotation (yes, no) of the mSASSS-related characteristics (erosion, sclerosis, squaring, syndesmophyte, bony bridging, spur, blurring) over the anterior corners of the cervical (C) and lumbar (L) spines from lateral views of radiographs. One radiologist (Lan-HHC) and two rheumatologists (Chen-HH, Chen-DY) annotate the characteristics and the final judgement was determined by a majority vote. We use PoseNet model develop a localization system identify spines (from second C-spine to first thoracic spines [C1-T1], from twelveth thoracic spine to first sacrum [T12-S1]). We developed a classification system using DenseNet model to identify the mSASSS-related specific characteristics and then calculate mSASSS. <h3>Results</h3> We randomly selected 211 pairs of radiographs as testing set. The remaining 845 (80%) pairs of images were used for model trainning and testing. Because the number of positive findings of erosion, sclerosis and squaring was quite low in both C-spine and L-spine, we combined radiographs of C-spine and L-spine for AI taining to score erosion, sclerosis and squaring. For detection of C-spine characterisitics, the area under curve (AUC)/specificity/sensitivity/accuracy for bridge, syndesmophyte and osteophyte were 99.8%/98.82%/97.44%/98.48%, 95.7%/94.2%/77.78%./91.67<i>%</i> and 97.5%/95.86%/84.16%/93.75%, respectively. For detection of L-spine chracteristics, the AUC/specificity/sensitivity/accuracy for bridge, syndesmophyte and osteophyte were 99.4%/98.46%/94.02%/97.36%, 94.00%/91.48%/77.97%./89.36<i>%</i> and 96.60%/91.08%/89.69%/90.82%, respectively. For detection of C/L-spine characteristics, the AUC/specificity/sensitivity/accuracy for sclerosis and squaring were 95.60%/93.91%/79.79%/91.58% and 94.40%/92.24%/77.56%./88.55<i>%</i>, respectively. We failed to develop an AI model to detect erosion due to its rarity (2 in C-spine and 8 in L-spine). Using the testing set of 211 paired C-spine and L-spine radiographs, the overall accuracy to detect mSASSS-related characteritics was 86.47%. <h3>Conclusion</h3> At this stage, the accuracy of the AI-assisted mSASSS scoring based on the perspective of the end user was 86.47%, which is expected to have good clinical practicability in TCVGH. Further testing of this mSASSS autocalculation AI model using radiographs from other hospitals is warranted for future clinical implementation in other hospitals. <h3>Reference</h3> [1]Koo BS, et al. Ther Adv Musculoskelet Dis 2022;14:1-9. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.